Modeling of Nanolubricant-Assisted Machining Process by using Multiple Regression Analysis. (14th February 2023)

• Record Type: Journal Article
• Authors: Srikiran, S.; Pavani, P. N. L.; Palani, Kumaran
• Other Names: Parvinzadeh Gashti Mazeyar Academic Editor.
• Is Part Of: Journal of nanomaterials. Volume 2023(2023)
• Publication Date: 2023-02-14
• Subjects: Nanostructured materials -- Periodicals; Nanotechnology -- Periodicals; Nanomatériaux; Nanostructured materials; Nanotechnology; Nanostructures; Nanotechnology; Periodicals; Fulltext; Internet Resources; Periodicals; 620.115
• DOI: 10.1155/2023/3669399
• Languages: English
• ISSNs: 1687-4110

Abstract

Abstract : Graphite, due to its hexagonally arranged crystal structure, is a preferred lubricant. The crystal structure within a planar condensed ring system indicates that the layers are stacked in a direction that is parallel to each other. Researchers have reported that the use of graphite powder as a lubricant during machining has exhibited promising results. Mixture of graphite in a carrying medium demonstrated multifunctional lubrication performance due to the separation of sliding surfaces by a liquid lubricant film and protected by solid powder. Scientific literature has pointed out that graphite powder at the nanoscale has been used in various mechanical operations exhibiting promising results. It is found that nanolevel graphite powder has been used previously by researchers in the metal-forming operations and tribological tests. This emphasizes the significance of the present work, which investigates the impact of nanoscale differences in the particle size of graphite powder has on the machining of hardened steel. With SAE 40 oil functioning as the carrying medium and nanocrystalline graphite of various size range performing as the lubricant, the current work attempts to determine the effects of solid-lubricant-assisted machining. It is observed experimentally that the machining parameters have improved with respect to the particle size of the nanopowder. The experimental results show that the cutting forces, tool temperatures, and surface roughness are found to increase as the size of the nanocrystalline graphite powder is reduced from 70–90 to 5–10 nm. Using the experimental values, regression analysis is carried out to develop nonlinear expressions between the input and output variables using SPSS statistical tool. The data are used to develop the models to predict cutting forces, tool temperatures, and surface roughness for the input parameters like size of the nanocrystalline graphite powder, depth of cut, feed rate, and cutting velocity for a considerably good range in a scientific way so that further researchers can use it. Further, the outputs obtained from the experimentation and the regression equations are compared and analysis is carried out in terms of the error percentage.